Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10129 publications
PaLI-X: On Scaling up a Multilingual Vision and Language Model
Josip Djolonga
Piotr Padlewski
Basil Mustafa
Carlos Riquelme
Sebastian Goodman
Yi Tay
Siamak Shakeri
Daniel Salz
Michael Tschannen
Mandar Joshi
Filip Pavetić
Gang Li
Anurag Arnab
Yuanzhong Xu
Keran Rong
Neil Houlsby
Computer Vision and Pattern Recognition Conference (CVPR) (2024)
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We explore the boundaries of scaling up a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image-based document understanding and few-shot (in-context) learning, as well as object detection, video question answering, and video captioning. Our model advances the state-of-the-art on most vision-and-language benchmarks considered (20+ of them). Finally, we observe emerging capabilities, such as complex counting and multilingual object detection, tasks that are not explicitly in the training mix.
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Batch Calibration: Rethinking Calibration For In-Context Learning And Prompt Engineering
Lev Proleev
Diana Mincu
International Conference on Learning Representations (ICLR) (2024)
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Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias factors in the prompt, including but not limited to the formatting, the choice verbalizers, and the ICL examples. To address this problem that results in unexpected performance degradation, calibration methods have been developed to mitigate the effects of these biases while recovering LLM performance. In this work, we first conduct a systematic analysis of the existing calibration methods, where we both provide a unified view and reveal the failure cases. Inspired by these analyses, we propose Batch Calibration (BC), a simple yet intuitive method that controls the contextual bias from the batched input, unifies various prior approaches, and effectively addresses the aforementioned issues. BC is zero-shot, inference-only, and incurs negligible additional costs. In the few-shot setup, we further extend BC to allow it to learn the contextual bias from labeled data. We validate the effectiveness of BC with PaLM 2-(S, M, L) and CLIP models and demonstrate state-of-the-art performance over previous calibration baselines across more than 10 natural language understanding and image classification tasks.
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TextMesh: Generation of Realistic 3D Meshes From Text Prompts
Christina Tsalicoglou
Fabian Manhardt
Michael Niemeyer
3DV 2024 (2024)
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The ability to generate highly realistic 2D images from mere text prompts has recently made huge progress in terms of speed and quality, thanks to the advent of image diffusion models. Naturally, the question arises if this can be also achieved in the generation of 3D content from such text prompts. To this end, a new line of methods recently emerged trying to harness diffusion models, trained on 2D images, for supervision of 3D model generation using view dependent prompts. While achieving impressive results, these methods, however, have two major drawbacks. First, rather than commonly used 3D meshes, they instead generate neural radiance fields (NeRFs), making them impractical for most real applications. Second, these approaches tend to produce over-saturated models, giving the output a cartoonish looking effect. Therefore, in this work we propose a novel method for generation of highly realistic-looking 3D meshes. To this end, we extend NeRF to employ an SDF backbone, leading to improved 3D mesh extraction. In addition, we propose a novel way to finetune the mesh texture, removing the effect of high saturation and improving the details of the output 3D mesh.
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BigLake: BigQuery’s Evolution toward a Multi-Cloud Lakehouse
Justin Levandoski
Garrett Casto
Mingge Deng
Rushabh Desai
Thibaud Hottelier
Amir Hormati
Jeff Johnson
Dawid Kurzyniec
Prem Ramanathan
Gaurav Saxena
Vidya Shanmugam
Yuri Volobuev
SIGMOD (2024)
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BigQuery’s cloud-native disaggregated architecture has allowed Google Cloud to evolve the system to meet several customer needs across the analytics and AI/ML workload spectrum. A key customer requirement for BigQuery centers around the unification of data lake and enterprise data warehousing workloads. This approach combines: (1) the need for core data management primitives, e.g., security, governance, common runtime metadata, performance acceleration, ACID transactions, provided by an enterprise data warehouses coupled with (2) harnessing the flexibility of the open source format and analytics ecosystem along with new workload types such as AI/ML over unstructured data on object storage. In addition, there is a strong requirement to support BigQuery as a multi-cloud offering given cloud customers are opting for a multi-cloud footprint by default.
This paper describes BigLake, an evolution of BigQuery toward a multi-cloud lakehouse to address these customer requirements in novel ways. We describe three main innovations in this space. We first present BigLake tables, making open-source table formats (e.g., Apache Parquet, Iceberg) first class citizens, providing fine-grained governance enforcement and performance acceleration over these formats to BigQuery and other open-source analytics engines. Next, we cover the design and implementation of BigLake Object tables that allow BigQuery to integrate AI/ML for inferencing and processing over unstructured data. Finally, we present Omni, a platform for deploying BigQuery on non-GCP clouds, focusing on the infrastructure and operational innovations we made to provide an enterprise lakehouse product regardless of the cloud provider hosting the data.
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Android Permissions: Evolution, Attacks, and Best Practices
IEEE Security & Privacy (2024)
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In this article, we study the evolution of Android permissions. We describe the rationale behind key changes in Android’s permission model and disclose two permission-related security vulnerabilities we discovered. Finally, we provide developers actionable insights to proactively address permission-related security and privacy risks during development.
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The evolution of AI is a pivotal moment in history, but it’s not the first time we have experienced technological advances that have changed how humans work. By looking at the advances in automobiles, we are reminded of the importance of focusing on our developers' needs and goals.
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Nteasee: A qualitative study of expert and general population perspectives on deploying AI for health in African countries
Iskandar Haykel
Florence Ofori
Kerrie Kauer
Tousif Ahmad
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Background: Artificial Intelligence for health has the potential to significantly change and improve healthcare. However in most African countries identifying culturally and contextually attuned approaches for deploying these solutions is not well understood. To bridge this gap, we conduct a qualitative study to investigate the best practices, fairness indicators and potential biases to mitigate when deploying AI for health in African countries, as well as explore opportunities where artificial intelligence could make a positive impact in health.
Methods: We used a mixed methods approach combining in-depth interviews (IDIs) and surveys. We conduct 1.5-2 hour long IDIs with 50 experts in health, policy and AI across 17 countries, and through an inductive approach we conduct a qualitative thematic analysis on expert IDI responses. We administer a blinded 30-minute survey with thought-cases to 672 general population participants across 5 countries in Africa (Ghana, South Africa, Rwanda, Kenya and Nigeria), and analyze responses on quantitative scales, statistically comparing responses by country, age, gender, and level of familiarity with AI. We thematically summarize open-ended responses from surveys.
Results and Conclusion: Our results find generally positive attitudes, high levels of trust, accompanied by moderate levels of concern among general population participants for AI usage for health in Africa. This contrasts with expert responses, where major themes revolved around trust/mistrust, AI ethics concerns, and systemic barriers to overcome, among others. This work presents the first-of-its-kind qualitative research study of the potential of AI for health in Africa with perspectives from both experts and the general population. We hope that this work guides policy makers and drives home the need for education and the inclusion of general population perspectives in decision-making around AI usage.
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Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis
Winter Conference on Applications of Computer Vision 2024 (2024) (to appear)
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We propose Hierarchical Text Spotter (HTS), the first method for the joint task of word-level text spotting and geometric layout analysis.
HTS can annotate text in images with a hierarchical representation of 4 levels: character, word, line, and paragraph.
The proposed HTS is characterized by two novel components:
(1) a Unified-Detector-Polygon (UDP) that produces Bezier Curve polygons of text lines and an affinity matrix for paragraph grouping between detected lines;
(2) a Line-to-Character-to-Word (L2C2W) recognizer that splits lines into characters and further merges them back into words.
HTS achieves state-of-the-art results on multiple word-level text spotting benchmark datasets as well as geometric layout analysis tasks.
Code will be released upon acceptance.
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Meta Lifelong-Learning With Selective and Task-Aware Adaptation
Thanapapas Horsuwan
Kasidis Kanwatchara
Boonserm Kijsirikul
Peerapon Vateekul
IEEE Access, 12 (2024), pp. 34099-34115
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Meta-learning has been applied to lifelong language learning due to its ability to find an optimal model for efficient adaptation to any learned tasks. Generally, meta lifelong-learning partially stores samples from seen tasks in a memory and selects some of them to train the model, refresh the knowledge, and adapt the model for inference. However, the sample selection for these steps was usually done in a sub-optimal manner in existing work. Hence, we propose MeLSTA (Meta Lifelong-Learning with Selective and Task-Aware Adaptation) to effectively select the samples based on task identifiers and the adaptation scores reflecting the model behavior after adaptation. The results show that MeLSTA enhances the accuracy by 1.2% over the state-of-the-art while significantly shrinking the training duration by over 6 times. Additionally, our in-depth analysis reveals the strengths and limitations of MeLSTA and existing work, providing useful insights for future designs of meta lifelong-learning for NLP.
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Media Mix Model Calibration With Bayesian Priors
Mike Wurm
Brenda Price
Ying Liu
research.google (2024)
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Effective model calibration is a critical and indispensable component in developing Media Mix Models (MMMs). One advantage of Bayesian-based MMMs lies in their capacity to accommodate the information from experiment results and the modelers' domain knowledge about the ad effectiveness by setting priors for the model parameters. However, it remains ambiguous about how and which Bayesian priors should be tuned for calibration purpose. In this paper, we propose a new calibration method through model reparameterization. The reparameterized model includes Return on Ads Spend (ROAS) as a model parameter, enabling straightforward adjustment of its prior distribution to align with either experiment results or the modeler's prior knowledge. The proposed method also helps address several key challenges regarding combining MMMs and incrementality experiments. We use simulations to demonstrate that our approach can significantly reduce the bias and uncertainty in the resultant posterior ROAS estimates.
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Analyzing Prospects for Quantum Advantage in Topological Data Analysis
Dominic W. Berry
Yuan Su
Casper Gyurik
Robbie King
Joao Basso
Abhishek Rajput
Nathan Wiebe
Vedran Djunko
PRX Quantum, 5 (2024), pp. 010319
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Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers in persistent homology (a way of characterizing topological features of data sets). Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples for which super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve some seemingly classically intractable instances.
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Generative Powers of Ten
Xiaojuan Wang
Steve Seitz
Ben Mildenhall
Pratul Srinivasan
Dor Verbin
Aleksander Hołyński
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We present a method that uses a text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene, e.g., ranging from a wide-angle landscape view of a forest to a macro shot of an insect sitting on one of the tree branches. This representation allows us to render continuously zooming videos, or explore different scales of the scene interactively. We achieve this through a joint multi-scale diffusion sampling approach that encourages consistency across different scales while preserving the integrity of each individual sampling process. Since each generated scale is guided by a different text prompt, our method enables deeper levels of zoom than traditional super-resolution methods that may struggle to create new contextual structure at vastly different scales. We compare our method qualitatively with alternative techniques in image super-resolution and outpainting, and show that our method is most effective at generating consistent multi-scale content.
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Assessing Web Fingerprinting Risk
Robert Busa-Fekete
Antonio Sartori
Proceedings of the ACM Web Conference (WWW 2024)
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Modern Web APIs allow developers to provide extensively customized experiences for website visitors, but the richness of the device information they provide also make them vulnerable to being abused by malign actors to construct browser fingerprints, device-specific identifiers that enable covert tracking of users even when cookies are disabled.
Previous research has established entropy, a measure of information, as the key metric for quantifying fingerprinting risk. Earlier studies that estimated the entropy of Web APIs were based on data from a single website or were limited to an extremely small sample of clients. They also analyzed each Web API separately and then summed their entropies to quantify overall fingerprinting risk, an approach that can lead to gross overestimates.
We provide the first study of browser fingerprinting which addresses the limitations of prior work. Our study is based on actual visited pages and Web API function calls reported by tens of millions of real Chrome browsers in-the-wild. We accounted for the dependencies and correlations among Web APIs, which is crucial for obtaining more realistic entropy estimates. We also developed a novel experimental design that accurately estimates entropy while never observing too much information from any single user. Our results provide an understanding of the distribution of entropy for different website categories, confirm the utility of entropy as a fingerprinting proxy, and offer a method for evaluating browser enhancements which are intended to mitigate fingerprinting.
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Almost no modern software system is written from scratch, and developers are required to effectively learn to use third-party libraries and software services. Thus, many practitioners and researchers have looked for ways to create effective documentation that supports developers’ learning. However, few efforts have focused on how people actually use the documentation. In this paper, we report on an exploratory, multi-phase, mixed methods empirical study of documentation page-view logs from four cloud-based industrial services. By analyzing page-view logs for over 100,000 users, we find diverse patterns of documentation page visits. Moreover, we show statistically that which documentation pages people visit often correlates with user characteristics such as past experience with the specific product, on the one hand, and with future adoption of the API on the other hand. We discuss the implications of these results on documentation design and propose documentation page-view log analysis as a feasible technique for design audits of documentation, from ones written for software developers to ones designed to support end users (e.g., Adobe Photoshop).
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Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
Yuchen Li
Alexandre Kirchmeyer
Aashay Mehta
Yilong Qin
Andrej Risteski
International Conference on Machine Learning (2024) (to appear)
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Autoregressive language models are the currently dominant paradigm for text generation, however they have some fundamental limitations that cannot be remedied by scale ---for example inherently sequential and unidirectional generation. While alternate classes of models have been explored, we have limited mathematical understanding of their fundamental power and limitations. In this paper we focus on Generative Masked Language Models (GMLMs), a non-autoregressive paradigm in which we train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. These models empirically strike a promising speed-quality trade-off as each step can be typically parallelized by decoding the entire sequence in parallel. We develop a mathematical framework for analyzing and improving such models which sheds light on questions of sample complexity and inference speed and quality. Empirically, we adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality compared with autoregressive models. We run careful ablation experiments to give recommendations on key design choices, and make fine-grained observations on the common error modes in connection with our theory. Our mathematical analyses and empirical observations characterize both potentials and limitations of this approach, and can be applied to future works on improving understanding and performance of GMLMs.
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